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The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

This study investigated the effect of pricing mechanism and oversubscription on the heterogeneity of investors' opinions on initial public offering (IPO) valuation.

Besides the ordinary least square method, this study incorporated robust least square, stepwise least square and quantile regression methods to investigate the aftermarket behaviour of investors using the price range on the first day of trading of 82 IPOs listed on the Pakistan stock exchange.

The aftermarket behaviour of investors was found to be significantly influenced by the pricing mechanism, oversubscription, financial leverage, political stability and the risk of IPO, whereas control of corruption showed an insignificant impact. Concurrently, the findings showed that pricing mechanism and oversubscription played a crucial role in determining the intensity of investors' heterogeneous opinions at high levels of significance.

Pricing mechanism and oversubscription not only signal the quality of IPOs but also provide an important means for reducing the information asymmetry associated with new listings. Based on the literature review, it was found that both the pricing mechanism and oversubscription have yet to be explored in investigating the aftermarket behaviour of investors using the price range in the Pakistan IPO market. This study suggests that book building pricing mechanism and oversubscription are associated with lower heterogeneity in investors’ opinions at a high level of significance.

This research investigates the bond between stock market development and agricultural growth in African emerging economies from 1990 to 2020.

Agricultural value added to the gross domestic product measures agricultural growth and market capitalization and stock value traded measure stock market development.

The findings disclose that market capitalization negatively affects agricultural growth while stock value traded positively affects agricultural growth in the fully modified and dynamic ordinary least square techniques. The findings unveil bidirectional causality between labour and agricultural value added with unidirectional causality flow from agricultural value added to market capitalization and stock value traded.

The governments should promote agricultural growth initiatives which stimulate stock market development. Effective methods required to encourage credit flow to the agricultural enterprises through the stock markets' intermediation should be promoted using aggressive policies which eliminate credit flow bottlenecks. Policy makers and regulatory authorities should implement policies which attract investors to the agricultural sector and encourage companies' listing in the stock markets. The capital market funding should be expanded to boost economic growth through agricultural value added.

Literature reveals divergent results on the relationship between stock market development and agricultural growth. Earlier studies provide conflicting findings on the bond between stock market development and agricultural growth. Some findings indicate positive link between stock market development and agricultural growth, while others show a negative association. Studies' results reveal opposing directions of causality between stock market development and agricultural growth.

This paper aims to study the improved effect of the instrumental variable method to estimate parameters of linear regression model with the stochastic explanatory variables problem.

By Monte‐Carlo method, taking a linear regression model with intercept of 3, slope of 4 as an example, whose random error in standard normal distribution, to test whether parameter estimators are biased and how about the average relative error of estimator of slope when random explanatory variables are in different contemporaneously correlated with random error item. By the instrumental variables which are independent with random error item and in varying degrees related to random explanatory variable, the study tests the estimation accuracy of the slope using the instrumental variable method.

This paper tests that the ordinary least square parameter estimators are biased, and especially that the average relative error of estimator of slope is significantly large, more than 10 percent, when random explanatory variables are different and contemporaneously correlated with the random error item. For the instrumental variables that are independent from random error item and in varying degrees related to the random explanatory variable, the estimation accuracy of the slope is significantly improved and the relative error dropped to less than 4 percent, but the estimation accuracy of the intercept term showed no significant improvement by the instrumental variable method.

The method exposed in the paper shows how to improve estimation by an instrumental variable method.

The paper succeeds in showing how to improve estimation by the instrumental variable method of numerical simulation.

The main purpose of this paper is to examine the relationships among chief executive officer (CEO) compensation, ownership and firm value. In addition, the determining factors of CEO compensation are examined.

This model is applied to data of the Taiwan stock market for 1995‐2004. The paper applies a two‐stage least squares regression for the panel data model and implements an F‐test, LM test and Hausman test to determine the best statistical method (that is, ordinary least squares method, fix effects model or random effects method).

The results offer some important insights that show CEO compensation, CEO ownership and firm value are interdependent. Firm size, board size, firm value, institution ownership and CEO ownership are positively associated with CEO compensation while firm age, research and development expenditure rates and firm risk are negatively associated with CEO compensation.

The on‐going expansion in the scale of the firm depends on managers having specialized knowledge. In particular, managers are responsible for the firm's entire operational conditions and future investment strategy. Providing an incentive compensation package can reduce agency costs between managers and shareholders. These findings also provide Taiwanese listed companies with a lesson, which suggests that the existence of the monitoring system can reduce the need for incentive alignment.

The study relies on data from publicly traded Taiwan firms, covering a ten‐year period. This study uses a simultaneous equation estimation procedure to investigate the relations among CEO compensation, CEO ownership and firm value. Two proxies for effective monitoring – board size and institutional ownership – are used. The paper attempts to discuss the influence on CEO compensation from the existence of the monitoring system.

The main purpose of this paper is to examine the determinants and interrelations between corporate ownership structure and corporate performance.

The ownership of institutional investors are classified into government institutional ownership, financial institutional ownership, securities investment trust funds ownership, incorporated companies ownership, and other institutional ownership, so as to facilitate a detailed study. Ten‐year (1994‐2003) panel data of 569 Taiwanese listed companies are examined. Furthermore, this study applies the F‐test, LM‐test and Hausman test to determine the best statistical method (ordinary least squares method, fix effects model or random effects method).

The results show an inverse “U‐shaped” relationship between insider ownership and corporate performance. Government institutional ownership and incorporated companies ownership are found to have a significant negative correlation with corporate performance. However, securities investment trust funds and corporate performance are positively correlated.

These findings provide Taiwanese listed companies with a insights on how to improve their corporate control mechanisms. These results can also serve as a useful reference for companies and the academics concerning future competitive strategies and decision making.

This study further incorporates the ratio of mortgaged/pledged shares of directors and supervisors and different institutional ownership to analyze how the mechanisms of corporate governance function in Taiwan's industries, and whether these mechanisms can effectively lower agency problems to enhance corporate performance.

India has mandated corporate social responsibility (CSR) expenditure under Section 135 of the Indian Companies Act, 2013 – the first national jurisdiction to do so. The purpose of this paper is to examine the impact of mandated CSR expenditure on firms’ stock returns by using actual CSR spending data, whereas the previous studies mostly focus on voluntary CSR proxied by CSR scores.

The authors estimate their baseline regression by using ordinary least squares(OLS) method. Although the baseline regression involving CSR expenditure and stock returns using ordinary least squares method are estimated, endogeneity and reverse causality biases are addressed by using two-stage least squares and generalized method of moments approaches. These approaches contribute mitigating endogeneity bias and biases associated with unobserved heterogeneity and simultaneity.

The findings document that mandatory CSR expenditure has a negative impact on firms’ stock returns which supports the “shareholders” expense’ view. This result remain robust after controlling for endogeneity bias and the use of both standard and robust test statistics. The authors however observe that this result holds for the firms with actual CSR expenditure equal to the mandated amount but does not hold for the firms with actual CSR expenditure greater than the mandated amount. Therefore, the authors provide evidence that CSR expenditure’s impact on stock returns depends on whether firms simply comply the regulation or voluntarily chose an amount of CSR expenditure above the mandated amount.

The primary contribution is to present a valid and robust evidence of negative effect of mandated CSR spending on firms’ stock returns when the mandatory CSR spending rule is already in place. This study contributes by examining the impact of mandated CSR spending on stock during post-implementation period (2015-2017), whereas other studies by Dharampala and Khanna (2018); Kapoor and Dhamija (2017); and Mukherjee et al. (2018) mainly examined the impact of legislation on Indian CSR. The authors use mandated actual CSR expenditure, whereas previous studies mostly focus on voluntary CSR proxied by CSR scores.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

This research aims to examine the impact of money laundering (ML) and corruption on the asset quality of conventional and Islamic banks.

The current study used the data of conventional and Islamic banks of Pakistan from 2012 to 2018. In this study, we used fully modified ordinary least squares, dynamic ordinary least squares and pooled ordinary least square methods to analyze the data.

The results found that corruption and ML positively affect the conventional banking non-performing loans (NPLs). In contrast, corruption and ML harm the Islamic bank’s loan portfolio quality.

To the best of the authors’ knowledge, the relationship between corruption, ML and NPLs in conventional and Islamic banks of Pakistan are examined for the first time.

According to the study’s findings, bank authorities should establish an effective method for monitoring loan activities and developing new and innovative products in Islamic banks. Additionally, the Pakistani government needs to improve anti-corruption and anti-ML policies to earn investors’ trust.

The purpose of this paper is to examine the linkage between agricultural sector foreign direct investment (FDI) and economic growth in Pakistan over the period from 1991 to 2013.

In this study, the stationary analysis is performed by using Phillips–Perron and Dickey–Fuller generalized least squares unit root tests and Johansen cointegration technique to determine the long-run linkage among the studied variables. The robustness of long-run linkage is checked by employing autoregressive distributed lag (ARDL) approach, dynamic ordinary least squares (DOLS), fully modified ordinary least square method (FMOLS) and the canonical cointegration regression (CCR). The causal linkage between the selected variables is investigated by the VECM Granger causality test.

The results of the Johansen cointegration test confirmed a cointegrating association between the variables. In addition, the results of the ARDL, DOLS, FMOLS and CCR showed that agricultural sector FDI has a strong positive significant effect on economic growth in long run. Moreover, the findings of the present empirical study revealed that there exists bidirectional Granger causality between the agricultural sector FDI and economic growth in both short run and long run.

The present empirical study filled the literature gap of applying the Granger causality based on error-correction model to examine this relevant issue for Pakistan.